Abstract: As the majority of faults are found in a few of its
modules so there is a need to investigate the modules that are
affected severely as compared to other modules and proper
maintenance need to be done in time especially for the critical
applications. As, Neural networks, which have been already applied
in software engineering applications to build reliability growth
models predict the gross change or reusability metrics. Neural
networks are non-linear sophisticated modeling techniques that are
able to model complex functions. Neural network techniques are
used when exact nature of input and outputs is not known. A key
feature is that they learn the relationship between input and output
through training. In this present work, various Neural Network Based
techniques are explored and comparative analysis is performed for
the prediction of level of need of maintenance by predicting level
severity of faults present in NASA-s public domain defect dataset.
The comparison of different algorithms is made on the basis of Mean
Absolute Error, Root Mean Square Error and Accuracy Values. It is
concluded that Generalized Regression Networks is the best
algorithm for classification of the software components into different
level of severity of impact of the faults. The algorithm can be used to
develop model that can be used for identifying modules that are
heavily affected by the faults.
Abstract: In this paper, we propose a hardware and software
design method for automotive Electronic Control Units (ECU)
considering the functional safety. The proposed ECU is considered for
the application to Electro-Mechanical Actuator systems and the
validity of the design method is shown by the application to the
Electro-Mechanical Brake (EMB) control system which is used as a
brake actuator in Brake-By-Wire (BBW) systems. The importance of a
functional safety-based design approach to EMB ECU design has been
emphasized because of its safety-critical functions, which are executed
with the aid of many electric actuators, sensors, and application
software. Based on hazard analysis and risk assessment according to
ISO26262, the EMB system should be ASIL-D-compliant, the highest
ASIL level. To this end, an external signature watchdog and an
Infineon 32-bit microcontroller TriCore are used to reduce risks
considering common-cause hardware failure. Moreover, a software
design method is introduced for implementing functional
safety-oriented monitoring functions based on an asymmetric dual
core architecture considering redundancy and diversity. The validity
of the proposed ECU design approach is verified by using the EMB
Hardware-In-the-Loop (HILS) system, which consists of the EMB
assembly, actuator ECU, a host PC, and a few debugging devices.
Furthermore, it is shown that the existing sensor fault tolerant control
system can be used more effectively for mitigating the effects of
hardware and software faults by applying the proposed ECU design
method.